Building automation systems with regional intelligence

ABSTRACT

A method for troubleshooting a building automation system includes selecting an artificial intelligence tool from a set of available artificial intelligence tools based on the geographic region of the building automation system, ranking troubleshooting options for the building automation system by applying the artificial intelligence tool to data associated with the building automation system, and implementing at least a first troubleshooting option, the first troubleshooting option ranked higher than a remainder of the troubleshooting options by the artificial intelligence tool.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to and the benefit of U.S. ProvisionalApplication No. 63/354,269 filed Jun. 22, 2022, the entire disclosure ofwhich is incorporated by reference herein.

BACKGROUND

The present disclosure relates generally to building management systems(building automation systems). The present disclosure relates moreparticularly to challenges associated with location of buildingmanagement systems across a wide variety of geographical regionals withdifferent climates, architectures, infrastructure, utilities, equipmenttypes, culture, terminology, languages, etc. For example, differences intechnical terminology used across geographic regions, even acrossdifferent regions having the same predominant language, can createconfusion when distributing or supporting building management systemsacross multiple regions. As another example, differences in climate,architecture, types of equipment used, etc. can cause configurationsettings, technician workflows (e.g., troubleshooting steps), etc. thatare useful in one region to be unhelpful in other regions. As such,approaches for automatically handling regional differences would bebeneficial.

A building management system (BMS) is, in general, a system of devicesconfigured to control, monitor, and manage equipment in or around abuilding or building area. A BMS can include a heating, ventilation, orair conditioning (HVAC) system, a security system, a lighting system, afire alerting system, another system that is capable of managingbuilding functions or devices, or any combination thereof. BMS devicesmay be installed in any environment (e.g., an indoor area or an outdoorarea) and the environment may include any number of buildings, spaces,zones, rooms, or areas. A BMS may include METASYS® building controllersor other devices sold by Johnson Controls, Inc., as well as buildingdevices and components from other sources.

A BMS may include one or more computer systems (e.g., servers, BMScontrollers, etc.) that serve as enterprise level controllers,application or data servers, head nodes, master controllers, or fieldcontrollers for the BMS. Such computer systems may communicate withmultiple downstream building systems or subsystems (e.g., an HVACsystem, a security system, etc.) according to like or disparateprotocols (e.g., LON, BACnet, etc.). The computer systems may alsoprovide one or more human-machine interfaces or client interfaces (e.g.,graphical user interfaces, reporting interfaces, text-based computerinterfaces, client-facing web services, web servers that provide pagesto web clients, etc.) for controlling, viewing, or otherwise interactingwith the BMS, its subsystems, and devices.

SUMMARY

One implementation of the present disclosure is a method fortroubleshooting a building automation system. The method includesselecting an artificial intelligence tool from a set of availableartificial intelligence tools based on the geographic region of thebuilding automation system, ranking troubleshooting options for thebuilding automation system by applying the artificial intelligence toolto data associated with the building automation system, and implementingat least a first troubleshooting option, the first troubleshootingoption ranked higher than a remainder of the troubleshooting options bythe artificial intelligence tool.

In some embodiments, the method also includes ignoring one or more ofthe troubleshooting options based on the geographic region of thebuilding automation system. Implementing at the least the firsttroubleshooting option can include automatically adjusting operation ofbuilding equipment operated by the building automation system.Implementing at the least the first troubleshooting option can includeadjusting an installation of one or more devices of the buildingautomation system. Implementing at the least the first troubleshootingoption can include changing configuration parameters of one or moredevices of the building automation system.

In some embodiments, the method includes displaying the firsttroubleshooting options to a user via a mobile application. In someembodiments, selecting the artificial intelligence tool is further basedon a domain of building equipment to be troubleshot.

Another implementation of the present disclosure is a method. The methodincludes communicating building automation information with a pluralityof building automation systems located in a plurality of geographicregions, automatically adjusting the building automation information inaccordance with different regional terminology used by the plurality ofbuilding automation systems, and controlling building equipment with atleast a first building automation system of the plurality of buildingautomation systems based on inputs provided by a first user in a firstgeographic region using a first regional terminology associated with thefirst geographic region and inputs provides by a second user in a secondgeographic region using a second regional terminology associated withthe second geographic region.

In some embodiments, the first building automation system and the firstuser are located at the first geographic region and the second user islocated at the second geographic region. The first user may beassociated with the first building automation system and the second usermay provide support for the plurality of building automation systems. Insome embodiments, automatically adjusting the building automationinformation in accordance with different regional terminology comprisesapplying a machine learning model to the building automationinformation.

In some embodiments, the method also includes prompting, via theplurality of building automation systems, users at the plurality ofgeographic regions to answer questions relating to the differentregional terminology used by the users and training the machine learningmodel based on answers to the questions.

In some embodiments, automatically adjusting the building automationinformation in accordance with different regional terminology used bythe plurality of building automation systems comprises normalizing pointlabels for data provided by the plurality of building automation systemsto facilitate analysis of aggregations of the data from the plurality ofbuilding automation systems.

BRIEF DESCRIPTION OF THE DRAWINGS

Various objects, aspects, features, and advantages of the disclosurewill become more apparent and better understood by referring to thedetailed description taken in conjunction with the accompanyingdrawings, in which like reference characters identify correspondingelements throughout. In the drawings, like reference numbers generallyindicate identical, functionally similar, and/or structurally similarelements.

FIG. 1 is a drawing of a building equipped with a building managementsystem (BMS), according to some embodiments.

FIG. 2 is a block diagram of a BMS that serves the building of FIG. 1 ,according to some embodiments.

FIG. 3 is a block diagram of a BMS controller which can be used in theBMS of FIG. 2 , according to some embodiments.

FIG. 4 is another block diagram of the BMS that serves the building ofFIG. 1 , according to some embodiments.

FIG. 5 is a block diagram of building automation systems serving avariety of geographic regions, according to some embodiments.

FIG. 6 is a flowchart of a region-based troubleshooting process,according to some embodiments.

DETAILED DESCRIPTION Overview

Referring generally to the FIGURES, approaches relating to artificialintelligence in the context of building management systems (or otherbuilding equipment or collections thereof) distributed across geographicregions are shown, according to some embodiments. One aspect of thepresent disclosure is a determination that artificial intelligence forbuilding systems will benefit from regional knowledge, that is,knowledge associated with or otherwise tuned for unique characteristicsof different geographic regions.

For example, in the context of commissioning, configuring, ortroubleshooting building equipment and building management systems,regional differences can have significant impacts on the relevantsettings, workflows, installations, etc. suitable for providing awell-functioning system. As one example, buildings in the Northeasternregion of the U.S. (e.g., Maine) often have hot water radiators to heatspaces, while buildings in the Midwest of the U.S. (e.g., Wisconsin)often uses natural gas furnaces and forced air heat and buildings in thesouthwest (e.g., Texas) often have electric heating systems. Because ofsuch differences, technicians from different regions would havedifficulty successful install, commission, troubleshooting, etc.building systems in other regions. The same challenge exists forartificial intelligence (AI) tools, which, if built and trained once bya manufacturer/designer in a geographically centralized or agnosticmanner, inherently lack regional knowledge required for successfuldeployment across geographic regions. The approaches herein solve suchchallenges, including by providing sets of region-specific AI modelsselectable based on the region of interest, thereby improving thetechnical field of building management systems.

As another example, different terminology may be used in differentregions to refer to the same physical parameters or equipment setpoints.Different terminology may result from different languages (e.g., Englishand French in different regions of Canada) or from variations within theindustry in the same language (e.g., HVAC technicians in Iowa may usedifferent terminology than technicians in New York or California). Suchlinguistic differences create confusion when distributing instructions,settings, dashboards, interfaces, recommendations, etc. from acentralized tool (e.g., manufacture support, software-as-a-service,etc.) to multiple geographic regions. The linguistic differences alsocreate challenges for processing data received from multiple geographicregions, in which case one aspect of the present disclosure is to use AItrained on regional differences to normalize data from multiple regionsto remove such linguistic differences and thereby enabling centralizedlearning and analysis from such aggregated dated and direct comparisonbetween data from different regions even where originally created usinglabels of different terminology. The approaches herein solve suchchallenges, thereby improving the technical field of building managementsystems.

Embodiments of the present disclosure are described in detail below. Insome embodiments, a set of available artificial intelligence models isprovided. Each artificial intelligence model may be associated with ageographic region. A process can include determining the location of aunit of equipment, building management system, or other relevant deviceand automatically selecting the artificial intelligence model associatedwith the geographic region including the location of the equipment, BMS,or device. The selected artificial intelligence model can then be usedonline to control, commission, troubleshoot, monitor, detect or diagnosefaults in, etc. the equipment, BMS, or device. Such an approach can beadapted for use with AI models in the context of fault detection anddiagnosis (e.g., as in U.S. patent application Ser. Nos. 17/710,443,17/710,597, 17/710,706, and 17/710,603, all filed Mar. 31, 2022, all ofwhich are incorporated by reference herein), control of buildingequipment (e.g., as in U.S. Pat. Pub. No. 20200355391 or U.S. Pat. No.10,901,373, incorporated by reference herein), and automatedconfiguration and other applications (e.g., as in U.S. ProvisionalPatent App. Nos. 63/279,759, 63/315,442, 63/315,454, and 63/315,459,incorporated by reference herein), for example providing instances ofsuch models for each of multiple geographic regions.

Building and Building Management System

Referring now to FIG. 1 , a perspective view of a building 10 is shown,according to an exemplary embodiment. A BMS serves building 10. The BMSfor building 10 may include any number or type of devices that servebuilding 10. For example, each floor may include one or more securitydevices, video surveillance cameras, fire detectors, smoke detectors,lighting systems, HVAC systems, or other building systems or devices. Inmodern BMSs, BMS devices can exist on different networks within thebuilding (e.g., one or more wireless networks, one or more wirednetworks, etc.) and yet serve the same building space or control loop.For example, BMS devices may be connected to different communicationsnetworks or field controllers even if the devices serve the same area(e.g., floor, conference room, building zone, tenant area, etc.) orpurpose (e.g., security, ventilation, cooling, heating, etc.).

BMS devices may collectively or individually be referred to as buildingequipment. Building equipment may include any number or type of BMSdevices within or around building 10. For example, building equipmentmay include controllers, chillers, rooftop units, fire and securitysystems, elevator systems, thermostats, lighting, serviceable equipment(e.g., vending machines), and/or any other type of equipment that can beused to control, automate, or otherwise contribute to an environment,state, or condition of building 10. The terms “BMS devices,” “BMSdevice” and “building equipment” are used interchangeably throughoutthis disclosure.

Referring now to FIG. 2 , a block diagram of a BMS 11 for building 10 isshown, according to an exemplary embodiment. BMS 11 is shown to includea plurality of BMS subsystems 20-26. Each BMS subsystem 20-26 isconnected to a plurality of BMS devices and makes data points forvarying connected devices available to upstream BMS controller 12.Additionally, BMS subsystems 20-26 may encompass other lower-levelsubsystems. For example, an HVAC system may be broken down further as“HVAC system A,” “HVAC system B,” etc. In some buildings, multiple HVACsystems or subsystems may exist in parallel and may not be a part of thesame HVAC system 20.

As shown in FIG. 2 , BMS 11 may include a HVAC system 20. HVAC system 20may control HVAC operations building 10. HVAC system 20 is shown toinclude a lower-level HVAC system 42 (named “HVAC system A”). HVACsystem 42 may control HVAC operations for a specific floor or zone ofbuilding 10. HVAC system 42 may be connected to air handling units(AHUs) 32, 34 (named “AHU A” and “AHU B,” respectively, in BMS 11). AHU32 may serve variable air volume (VAV) boxes 38, 40 (named “VAV 3” and“VAV 4” in BMS 11). Likewise, AHU 34 may serve VAV boxes 36 and 110(named “VAV 2” and “VAV 1”). HVAC system 42 may also include chiller 30(named “Chiller A” in BMS 11). Chiller 30 may provide chilled fluid toAHU 32 and/or to AHU 34. HVAC system 42 may receive data (i.e., BMSinputs such as temperature sensor readings, damper positions,temperature setpoints, etc.) from AHUs 32, 34. HVAC system 42 mayprovide such BMS inputs to HVAC system 20 and on to middleware 14 andBMS controller 12. Similarly, other BMS subsystems may receive inputsfrom other building devices or objects and provide the received inputsto BMS controller 12 (e.g., via middleware 14).

Middleware 14 may include services that allow interoperablecommunication to, from, or between disparate BMS subsystems 20-26 of BMS11 (e.g., HVAC systems from different manufacturers, HVAC systems thatcommunicate according to different protocols, security/fire systems, ITresources, door access systems, etc.). Middleware 14 may be, forexample, an EnNet server sold by Johnson Controls, Inc. While middleware14 is shown as separate from BMS controller 12, middleware 14 and BMScontroller 12 may integrated in some embodiments. For example,middleware 14 may be a part of BMS controller 12.

Still referring to FIG. 2 , window control system 22 may receive shadecontrol information from one or more shade controls, ambient light levelinformation from one or more light sensors, and/or other BMS inputs(e.g., sensor information, setpoint information, current stateinformation, etc.) from downstream devices. Window control system 22 mayinclude window controllers 107, 108 (e.g., named “local windowcontroller A” and “local window controller B,” respectively, in BMS 11).Window controllers 107, 108 control the operation of subsets of windowcontrol system 22. For example, window controller 108 may control windowblind or shade operations for a given room, floor, or building in theBMS.

Lighting system 24 may receive lighting related information from aplurality of downstream light controls (e.g., from room lighting 104).Door access system 26 may receive lock control, motion, state, or otherdoor related information from a plurality of downstream door controls.Door access system 26 is shown to include door access pad 106 (named“Door Access Pad 3F”), which may grant or deny access to a buildingspace (e.g., a floor, a conference room, an office, etc.) based onwhether valid user credentials are scanned or entered (e.g., via akeypad, via a badge-scanning pad, etc.).

BMS subsystems 20-26 may be connected to BMS controller 12 viamiddleware 14 and may be configured to provide BMS controller 12 withBMS inputs from various BMS subsystems 20-26 and their varyingdownstream devices. BMS controller 12 may be configured to makedifferences in building subsystems transparent at the human-machineinterface or client interface level (e.g., for connected or hosted userinterface (UI) clients 16, remote applications 18, etc.). BMS controller12 may be configured to describe or model different building devices andbuilding subsystems using common or unified objects (e.g., softwareobjects stored in memory) to help provide the transparency. Softwareequipment objects may allow developers to write applications capable ofmonitoring and/or controlling various types of building equipmentregardless of equipment-specific variations (e.g., equipment model,equipment manufacturer, equipment version, etc.). Software buildingobjects may allow developers to write applications capable of monitoringand/or controlling building zones on a zone-by-zone level regardless ofthe building subsystem makeup.

Referring now to FIG. 3 , a block diagram illustrating a portion of BMS11 in greater detail is shown, according to an exemplary embodiment.Particularly, FIG. 3 illustrates a portion of BMS 11 that services aconference room 102 of building 10 (named “B1_F3_CR5”). Conference room102 may be affected by many different building devices connected to manydifferent BMS subsystems. For example, conference room 102 includes oris otherwise affected by VAV box 110, window controller 108 (e.g., ablind controller), a system of lights 104 (named “Room Lighting 17”),and a door access pad 106.

Each of the building devices shown at the top of FIG. 3 may includelocal control circuitry configured to provide signals to theirsupervisory controllers or more generally to the BMS subsystems 20-26.The local control circuitry of the building devices shown at the top ofFIG. 3 may also be configured to receive and respond to control signals,commands, setpoints, or other data from their supervisory controllers.For example, the local control circuitry of VAV box 110 may includecircuitry that affects an actuator in response to control signalsreceived from a field controller that is a part of HVAC system 20.Window controller 108 may include circuitry that affects windows orblinds in response to control signals received from a field controllerthat is part of window control system (WCS) 22. Room lighting 104 mayinclude circuitry that affects the lighting in response to controlsignals received from a field controller that is part of lighting system24. Access pad 106 may include circuitry that affects door access (e.g.,locking or unlocking the door) in response to control signals receivedfrom a field controller that is part of door access system 26.

Still referring to FIG. 3 , BMS controller 12 is shown to include a BMSinterface 132 in communication with middleware 14. In some embodiments,BMS interface 132 is a communications interface. For example, BMSinterface 132 may include wired or wireless interfaces (e.g., jacks,antennas, transmitters, receivers, transceivers, wire terminals, etc.)for conducting data communications with various systems, devices, ornetworks. BMS interface 132 can include an Ethernet card and port forsending and receiving data via an Ethernet-based communications network.In another example, BMS interface 132 includes a Wi-Fi transceiver forcommunicating via a wireless communications network. BMS interface 132may be configured to communicate via local area networks or wide areanetworks (e.g., the Internet, a building WAN, etc.).

In some embodiments, BMS interface 132 and/or middleware 14 includes anapplication gateway configured to receive input from applicationsrunning on client devices. For example, BMS interface 132 and/ormiddleware 14 may include one or more wireless transceivers (e.g., aWi-Fi transceiver, a Bluetooth transceiver, a NFC transceiver, acellular transceiver, etc.) for communicating with client devices. BMSinterface 132 may be configured to receive building management inputsfrom middleware 14 or directly from one or more BMS subsystems 20-26.BMS interface 132 and/or middleware 14 can include any number ofsoftware buffers, queues, listeners, filters, translators, or othercommunications-supporting services.

Still referring to FIG. 3 , BMS controller 12 is shown to include aprocessing circuit 134 including a processor 136 and memory 138.Processor 136 may be a general purpose or specific purpose processor, anapplication specific integrated circuit (ASIC), one or more fieldprogrammable gate arrays (FPGAs), a group of processing components, orother suitable processing components. Processor 136 is configured toexecute computer code or instructions stored in memory 138 or receivedfrom other computer readable media (e.g., CDROM, network storage, aremote server, etc.).

Memory 138 may include one or more devices (e.g., memory units, memorydevices, storage devices, etc.) for storing data and/or computer codefor completing and/or facilitating the various processes described inthe present disclosure. Memory 138 may include random access memory(RAM), read-only memory (ROM), hard drive storage, temporary storage,non-volatile memory, flash memory, optical memory, or any other suitablememory for storing software objects and/or computer instructions. Memory138 may include database components, object code components, scriptcomponents, or any other type of information structure for supportingthe various activities and information structures described in thepresent disclosure. Memory 138 may be communicably connected toprocessor 136 via processing circuit 134 and may include computer codefor executing (e.g., by processor 136) one or more processes describedherein. When processor 136 executes instructions stored in memory 138for completing the various activities described herein, processor 136generally configures BMS controller 12 (and more particularly processingcircuit 134) to complete such activities.

Still referring to FIG. 3 , memory 138 is shown to include buildingobjects 142. In some embodiments, BMS controller 12 uses buildingobjects 142 to group otherwise ungrouped or unassociated devices so thatthe group may be addressed or handled by applications together and in aconsistent manner (e.g., a single user interface for controlling all ofthe BMS devices that affect a particular building zone or room).Building objects can apply to spaces of any granularity. For example, abuilding object can represent an entire building, a floor of a building,or individual rooms on each floor. In some embodiments, BMS controller12 creates and/or stores a building object in memory 138 for each zoneor room of building 10. Building objects 142 can be accessed by UIclients 16 and remote applications 18 to provide a comprehensive userinterface for controlling and/or viewing information for a particularbuilding zone. Building objects 142 may be created by building objectcreation module 152 and associated with equipment objects by objectrelationship module 158, described in greater detail below.

Still referring to FIG. 3 , memory 138 is shown to include equipmentdefinitions 140. Equipment definitions 140 stores the equipmentdefinitions for various types of building equipment. Each equipmentdefinition may apply to building equipment of a different type. Forexample, equipment definitions 140 may include different equipmentdefinitions for variable air volume modular assemblies (VMAs), fan coilunits, air handling units (AHUs), lighting fixtures, water pumps, and/orother types of building equipment.

Equipment definitions 140 define the types of data points that aregenerally associated with various types of building equipment. Forexample, an equipment definition for VMA may specify data point typessuch as room temperature, damper position, supply air flow, and/or othertypes data measured or used by the VMA. Equipment definitions 140 allowfor the abstraction (e.g., generalization, normalization, broadening,etc.) of equipment data from a specific BMS device so that the equipmentdata can be applied to a room or space.

Each of equipment definitions 140 may include one or more pointdefinitions. Each point definition may define a data point of aparticular type and may include search criteria for automaticallydiscovering and/or identifying data points that satisfy the pointdefinition. An equipment definition can be applied to multiple pieces ofbuilding equipment of the same general type (e.g., multiple differentVMA controllers). When an equipment definition is applied to a BMSdevice, the search criteria specified by the point definitions can beused to automatically identify data points provided by the BMS devicethat satisfy each point definition.

In some embodiments, equipment definitions 140 define data point typesas generalized types of data without regard to the model, manufacturer,vendor, or other differences between building equipment of the samegeneral type. The generalized data points defined by equipmentdefinitions 140 allows each equipment definition to be referenced by orapplied to multiple different variants of the same type of buildingequipment.

In some embodiments, equipment definitions 140 facilitate thepresentation of data points in a consistent and user-friendly manner.For example, each equipment definition may define one or more datapoints that are displayed via a user interface. The displayed datapoints may be a subset of the data points defined by the equipmentdefinition.

In some embodiments, equipment definitions 140 specify a system type(e.g., HVAC, lighting, security, fire, etc.), a system sub-type (e.g.,terminal units, air handlers, central plants), and/or data category(e.g., critical, diagnostic, operational) associated with the buildingequipment defined by each equipment definition. Specifying suchattributes of building equipment at the equipment definition levelallows the attributes to be applied to the building equipment along withthe equipment definition when the building equipment is initiallydefined. Building equipment can be filtered by various attributesprovided in the equipment definition to facilitate the reporting andmanagement of equipment data from multiple building systems.

Equipment definitions 140 can be automatically created by abstractingthe data points provided by archetypal controllers (e.g., typical orrepresentative controllers) for various types of building equipment. Insome embodiments, equipment definitions 140 are created by equipmentdefinition module 154, described in greater detail below.

Still referring to FIG. 3 , memory 138 is shown to include equipmentobjects 144. Equipment objects 144 may be software objects that define amapping between a data point type (e.g., supply air temperature, roomtemperature, damper position) and an actual data point (e.g., a measuredor calculated value for the corresponding data point type) for variouspieces of building equipment. Equipment objects 144 may facilitate thepresentation of equipment-specific data points in an intuitive anduser-friendly manner by associating each data point with an attributeidentifying the corresponding data point type. The mapping provided byequipment objects 144 may be used to associate a particular data valuemeasured or calculated by BMS 11 with an attribute that can be displayedvia a user interface.

Equipment objects 144 can be created (e.g., by equipment object creationmodule 156) by referencing equipment definitions 140. For example, anequipment object can be created by applying an equipment definition tothe data points provided by a BMS device. The search criteria includedin an equipment definition can be used to identify data points of thebuilding equipment that satisfy the point definitions. A data point thatsatisfies a point definition can be mapped to an attribute of theequipment object corresponding to the point definition.

Each equipment object may include one or more attributes defined by thepoint definitions of the equipment definition used to create theequipment object. For example, an equipment definition which defines theattributes “Occupied Command,” “Room Temperature,” and “Damper Position”may result in an equipment object being created with the sameattributes. The search criteria provided by the equipment definition areused to identify and map data points associated with a particular BMSdevice to the attributes of the equipment object. The creation ofequipment objects is described in greater detail below with reference toequipment object creation module 156.

Equipment objects 144 may be related with each other and/or withbuilding objects 142. Causal relationships can be established betweenequipment objects to link equipment objects to each other. For example,a causal relationship can be established between a VMA and an AHU whichprovides airflow to the VMA. Causal relationships can also beestablished between equipment objects 144 and building objects 142. Forexample, equipment objects 144 can be associated with building objects142 representing particular rooms or zones to indicate that theequipment object serves that room or zone. Relationships between objectsare described in greater detail below with reference to objectrelationship module 158.

Still referring to FIG. 3 , memory 138 is shown to include clientservices 146 and application services 148. Client services 146 may beconfigured to facilitate interaction and/or communication between BMScontroller 12 and various internal or external clients or applications.For example, client services 146 may include web services or applicationprogramming interfaces available for communication by UI clients 16 andremote applications 18 (e.g., applications running on a mobile device,energy monitoring applications, applications allowing a user to monitorthe performance of the BMS, automated fault detection and diagnosticssystems, etc.). Application services 148 may facilitate direct orindirect communications between remote applications 18, localapplications 150, and BMS controller 12. For example, applicationservices 148 may allow BMS controller 12 to communicate (e.g., over acommunications network) with remote applications 18 running on mobiledevices and/or with other BMS controllers.

In some embodiments, application services 148 facilitate an applicationsgateway for conducting electronic data communications with UI clients 16and/or remote applications 18. For example, application services 148 maybe configured to receive communications from mobile devices and/or BMSdevices. Client services 146 may provide client devices with a graphicaluser interface that consumes data points and/or display data defined byequipment definitions 140 and mapped by equipment objects 144.

Still referring to FIG. 3 , memory 138 is shown to include a buildingobject creation module 152. Building object creation module 152 may beconfigured to create the building objects stored in building objects142. Building object creation module 152 may create a software buildingobject for various spaces within building 10. Building object creationmodule 152 can create a building object for a space of any size orgranularity. For example, building object creation module 152 can createa building object representing an entire building, a floor of abuilding, or individual rooms on each floor. In some embodiments,building object creation module 152 creates and/or stores a buildingobject in memory 138 for each zone or room of building 10.

The building objects created by building object creation module 152 canbe accessed by UI clients 16 and remote applications 18 to provide acomprehensive user interface for controlling and/or viewing informationfor a particular building zone. Building objects 142 can group otherwiseungrouped or unassociated devices so that the group may be addressed orhandled by applications together and in a consistent manner (e.g., asingle user interface for controlling all of the BMS devices that affecta particular building zone or room). In some embodiments, buildingobject creation module 152 uses the systems and methods described inU.S. patent application Ser. No. 12/887,390, filed Sep. 21, 2010, forcreating software defined building objects.

In some embodiments, building object creation module 152 provides a userinterface for guiding a user through a process of creating buildingobjects. For example, building object creation module 152 may provide auser interface to client devices (e.g., via client services 146) thatallows a new space to be defined. In some embodiments, building objectcreation module 152 defines spaces hierarchically. For example, the userinterface for creating building objects may prompt a user to create aspace for a building, for floors within the building, and/or for roomsor zones within each floor.

In some embodiments, building object creation module 152 createsbuilding objects automatically or semi-automatically. For example,building object creation module 152 may automatically define and createbuilding objects using data imported from another data source (e.g.,user view folders, a table, a spreadsheet, etc.). In some embodiments,building object creation module 152 references an existing hierarchy forBMS 11 to define the spaces within building 10. For example, BMS 11 mayprovide a listing of controllers for building 10 (e.g., as part of anetwork of data points) that have the physical location (e.g., roomname) of the controller in the name of the controller itself. Buildingobject creation module 152 may extract room names from the names of BMScontrollers defined in the network of data points and create buildingobjects for each extracted room. Building objects may be stored inbuilding objects 142.

Still referring to FIG. 3 , memory 138 is shown to include an equipmentdefinition module 154. Equipment definition module 154 may be configuredto create equipment definitions for various types of building equipmentand to store the equipment definitions in equipment definitions 140. Insome embodiments, equipment definition module 154 creates equipmentdefinitions by abstracting the data points provided by archetypalcontrollers (e.g., typical or representative controllers) for varioustypes of building equipment. For example, equipment definition module154 may receive a user selection of an archetypal controller via a userinterface. The archetypal controller may be specified as a user input orselected automatically by equipment definition module 154. In someembodiments, equipment definition module 154 selects an archetypalcontroller for building equipment associated with a terminal unit suchas a VMA.

Equipment definition module 154 may identify one or more data pointsassociated with the archetypal controller. Identifying one or more datapoints associated with the archetypal controller may include accessing anetwork of data points provided by BMS 11. The network of data pointsmay be a hierarchical representation of data points that are measured,calculated, or otherwise obtained by various BMS devices. BMS devicesmay be represented in the network of data points as nodes of thehierarchical representation with associated data points depending fromeach BMS device. Equipment definition module 154 may find the nodecorresponding to the archetypal controller in the network of data pointsand identify one or more data points which depend from the archetypalcontroller node.

Equipment definition module 154 may generate a point definition for eachidentified data point of the archetypal controller. Each pointdefinition may include an abstraction of the corresponding data pointthat is applicable to multiple different controllers for the same typeof building equipment. For example, an archetypal controller for aparticular VMA (i.e., “VMA-20”) may be associated an equipment-specificdata point such as “VMA-20.DPR-POS” (i.e., the damper position ofVMA-20) and/or “VMA-20.SUP-FLOW” (i.e., the supply air flow rate throughVMA-20). Equipment definition module 154 abstract the equipment-specificdata points to generate abstracted data point types that are generallyapplicable to other equipment of the same type. For example, equipmentdefinition module 154 may abstract the equipment-specific data point“VMA-20.DPR-POS” to generate the abstracted data point type “DPR-POS”and may abstract the equipment-specific data point “VMA-20.SUP-FLOW” togenerate the abstracted data point type “SUP-FLOW.” Advantageously, theabstracted data point types generated by equipment definition module 154can be applied to multiple different variants of the same type ofbuilding equipment (e.g., VMAs from different manufacturers, VMAs havingdifferent models or output data formats, etc.).

In some embodiments, equipment definition module 154 generates auser-friendly label for each point definition. The user-friendly labelmay be a plain text description of the variable defined by the pointdefinition. For example, equipment definition module 154 may generatethe label “Supply Air Flow” for the point definition corresponding tothe abstracted data point type “SUP-FLOW” to indicate that the datapoint represents a supply air flow rate through the VMA. The labelsgenerated by equipment definition module 154 may be displayed inconjunction with data values from BMS devices as part of a user-friendlyinterface.

In some embodiments, equipment definition module 154 generates searchcriteria for each point definition. The search criteria may include oneor more parameters for identifying another data point (e.g., a datapoint associated with another controller of BMS 11 for the same type ofbuilding equipment) that represents the same variable as the pointdefinition. Search criteria may include, for example, an instance numberof the data point, a network address of the data point, and/or a networkpoint type of the data point.

In some embodiments, search criteria include a text string abstractedfrom a data point associated with the archetypal controller. Forexample, equipment definition module 154 may generate the abstractedtext string “SUP-FLOW” from the equipment-specific data point“VMA-20.SUP-FLOW.” Advantageously, the abstracted text string matchesother equipment-specific data points corresponding to the supply airflow rates of other BMS devices (e.g., “VMA-18.SUP-FLOW,” “SUP-FLOW.VMA-01,” etc.). Equipment definition module 154 may store a name, label,and/or search criteria for each point definition in memory 138.

Equipment definition module 154 may use the generated point definitionsto create an equipment definition for a particular type of buildingequipment (e.g., the same type of building equipment associated with thearchetypal controller). The equipment definition may include one or moreof the generated point definitions. Each point definition defines apotential attribute of BMS devices of the particular type and providessearch criteria for identifying the attribute among other data pointsprovided by such BMS devices.

In some embodiments, the equipment definition created by equipmentdefinition module 154 includes an indication of display data for BMSdevices that reference the equipment definition. Display data may defineone or more data points of the BMS device that will be displayed via auser interface. In some embodiments, display data are user defined. Forexample, equipment definition module 154 may prompt a user to select oneor more of the point definitions included in the equipment definition tobe represented in the display data. Display data may include theuser-friendly label (e.g., “Damper Position”) and/or short name (e.g.,“DPR-POS”) associated with the selected point definitions.

In some embodiments, equipment definition module 154 provides avisualization of the equipment definition via a graphical userinterface. The visualization of the equipment definition may include apoint definition portion which displays the generated point definitions,a user input portion configured to receive a user selection of one ormore of the point definitions displayed in the point definition portion,and/or a display data portion which includes an indication of anabstracted data point corresponding to each of the point definitionsselected via the user input portion. The visualization of the equipmentdefinition can be used to add, remove, or change point definitionsand/or display data associated with the equipment definitions.

Equipment definition module 154 may generate an equipment definition foreach different type of building equipment in BMS 11 (e.g., VMAs,chillers, AHUs, etc.). Equipment definition module 154 may store theequipment definitions in a data storage device (e.g., memory 138,equipment definitions 140, an external or remote data storage device,etc.).

Still referring to FIG. 3 , memory 138 is shown to include an equipmentobject creation module 156. Equipment object creation module 156 may beconfigured to create equipment objects for various BMS devices. In someembodiments, equipment object creation module 156 creates an equipmentobject by applying an equipment definition to the data points providedby a BMS device. For example, equipment object creation module 156 mayreceive an equipment definition created by equipment definition module154. Receiving an equipment definition may include loading or retrievingthe equipment definition from a data storage device.

In some embodiments, equipment object creation module 156 determineswhich of a plurality of equipment definitions to retrieve based on thetype of BMS device used to create the equipment object. For example, ifthe BMS device is a VMA, equipment object creation module 156 mayretrieve the equipment definition for VMAs; whereas if the BMS device isa chiller, equipment object creation module 156 may retrieve theequipment definition for chillers. The type of BMS device to which anequipment definition applies may be stored as an attribute of theequipment definition. Equipment object creation module 156 may identifythe type of BMS device being used to create the equipment object andretrieve the corresponding equipment definition from the data storagedevice.

In other embodiments, equipment object creation module 156 receives anequipment definition prior to selecting a BMS device. Equipment objectcreation module 156 may identify a BMS device of BMS 11 to which theequipment definition applies. For example, equipment object creationmodule 156 may identify a BMS device that is of the same type ofbuilding equipment as the archetypal BMS device used to generate theequipment definition. In various embodiments, the BMS device used togenerate the equipment object may be selected automatically (e.g., byequipment object creation module 156), manually (e.g., by a user) orsemi-automatically (e.g., by a user in response to an automated promptfrom equipment object creation module 156).

In some embodiments, equipment object creation module 156 creates anequipment discovery table based on the equipment definition. Forexample, equipment object creation module 156 may create an equipmentdiscovery table having attributes (e.g., columns) corresponding to thevariables defined by the equipment definition (e.g., a damper positionattribute, a supply air flow rate attribute, etc.). Each column of theequipment discovery table may correspond to a point definition of theequipment definition. The equipment discovery table may have columnsthat are categorically defined (e.g., representing defined variables)but not yet mapped to any particular data points.

Equipment object creation module 156 may use the equipment definition toautomatically identify one or more data points of the selected BMSdevice to map to the columns of the equipment discovery table. Equipmentobject creation module 156 may search for data points of the BMS devicethat satisfy one or more of the point definitions included in theequipment definition. In some embodiments, equipment object creationmodule 156 extracts a search criterion from each point definition of theequipment definition. Equipment object creation module 156 may access adata point network of the building automation system to identify one ormore data points associated with the selected BMS device. Equipmentobject creation module 156 may use the extracted search criterion todetermine which of the identified data points satisfy one or more of thepoint definitions.

In some embodiments, equipment object creation module 156 automaticallymaps (e.g., links, associates, relates, etc.) the identified data pointsof selected BMS device to the equipment discovery table. A data point ofthe selected BMS device may be mapped to a column of the equipmentdiscovery table in response to a determination by equipment objectcreation module 156 that the data point satisfies the point definition(e.g., the search criteria) used to generate the column. For example, ifa data point of the selected BMS device has the name “VMA-18.SUP-FLOW”and a search criterion is the text string “SUP-FLOW,” equipment objectcreation module 156 may determine that the search criterion is met.Accordingly, equipment object creation module 156 may map the data pointof the selected BMS device to the corresponding column of the equipmentdiscovery table.

Advantageously, equipment object creation module 156 may create multipleequipment objects and map data points to attributes of the createdequipment objects in an automated fashion (e.g., without humanintervention, with minimal human intervention, etc.). The searchcriteria provided by the equipment definition facilitates the automaticdiscovery and identification of data points for a plurality of equipmentobject attributes. Equipment object creation module 156 may label eachattribute of the created equipment objects with a device-independentlabel derived from the equipment definition used to create the equipmentobject. The equipment objects created by equipment object creationmodule 156 can be viewed (e.g., via a user interface) and/or interpretedby data consumers in a consistent and intuitive manner regardless ofdevice-specific differences between BMS devices of the same generaltype. The equipment objects created by equipment object creation module156 may be stored in equipment objects 144.

Still referring to FIG. 3 , memory 138 is shown to include an objectrelationship module 158. Object relationship module 158 may beconfigured to establish relationships between equipment objects 144. Insome embodiments, object relationship module 158 establishes causalrelationships between equipment objects 144 based on the ability of oneBMS device to affect another BMS device. For example, objectrelationship module 158 may establish a causal relationship between aterminal unit (e.g., a VMA) and an upstream unit (e.g., an AHU, achiller, etc.) which affects an input provided to the terminal unit(e.g., air flow rate, air temperature, etc.).

Object relationship module 158 may establish relationships betweenequipment objects 144 and building objects 142 (e.g., spaces). Forexample, object relationship module 158 may associate equipment objects144 with building objects 142 representing particular rooms or zones toindicate that the equipment object serves that room or zone. In someembodiments, object relationship module 158 provides a user interfacethrough which a user can define relationships between equipment objects144 and building objects 142. For example, a user can assignrelationships in a “drag and drop” fashion by dragging and dropping abuilding object and/or an equipment object into a “serving” cell of anequipment object provided via the user interface to indicate that theBMS device represented by the equipment object serves a particular spaceor BMS device.

Still referring to FIG. 3 , memory 138 is shown to include a buildingcontrol services module 160. Building control services module 160 may beconfigured to automatically control BMS 11 and the various subsystemsthereof. Building control services module 160 may utilize closed loopcontrol, feedback control, PI control, model predictive control, or anyother type of automated building control methodology to control theenvironment (e.g., a variable state or condition) within building 10.

Building control services module 160 may receive inputs from sensorydevices (e.g., temperature sensors, pressure sensors, flow rate sensors,humidity sensors, electric current sensors, cameras, radio frequencysensors, microphones, etc.), user input devices (e.g., computerterminals, client devices, user devices, etc.) or other data inputdevices via BMS interface 132. Building control services module 160 mayapply the various inputs to a building energy use model and/or a controlalgorithm to determine an output for one or more building controldevices (e.g., dampers, air handling units, chillers, boilers, fans,pumps, etc.) in order to affect a variable state or condition withinbuilding 10 (e.g., zone temperature, humidity, air flow rate, etc.).

In some embodiments, building control services module 160 is configuredto control the environment of building 10 on a zone-individualizedlevel. For example, building control services module 160 may control theenvironment of two or more different building zones using differentsetpoints, different constraints, different control methodology, and/ordifferent control parameters. Building control services module 160 mayoperate BMS 11 to maintain building conditions (e.g., temperature,humidity, air quality, etc.) within a setpoint range, to optimize energyperformance (e.g., to minimize energy consumption, to minimize energycost, etc.), and/or to satisfy any constraint or combination ofconstraints as may be desirable for various implementations.

In some embodiments, building control services module 160 uses thelocation of various BMS devices to translate an input received from abuilding system into an output or control signal for the buildingsystem. Building control services module 160 may receive locationinformation for BMS devices and automatically set or recommend controlparameters for the BMS devices based on the locations of the BMSdevices. For example, building control services module 160 mayautomatically set a flow rate setpoint for a VAV box based on the sizeof the building zone in which the VAV box is located.

Building control services module 160 may determine which of a pluralityof sensors to use in conjunction with a feedback control loop based onthe locations of the sensors within building 10. For example, buildingcontrol services module 160 may use a signal from a temperature sensorlocated in a building zone as a feedback signal for controlling thetemperature of the building zone in which the temperature sensor islocated.

In some embodiments, building control services module 160 automaticallygenerates control algorithms for a controller or a building zone basedon the location of the zone in the building 10. For example, buildingcontrol services module 160 may be configured to predict a change indemand resulting from sunlight entering through windows based on theorientation of the building and the locations of the building zones(e.g., east-facing, west-facing, perimeter zones, interior zones, etc.).

Building control services module 160 may use zone location informationand interactions between adjacent building zones (rather thanconsidering each zone as an isolated system) to more efficiently controlthe temperature and/or airflow within building 10. For control loopsthat are conducted at a larger scale (i.e., floor level) buildingcontrol services module 160 may use the location of each building zoneand/or BMS device to coordinate control functionality between buildingzones. For example, building control services module 160 may considerheat exchange and/or air exchange between adjacent building zones as afactor in determining an output control signal for the building zones.

In some embodiments, building control services module 160 is configuredto optimize the energy efficiency of building 10 using the locations ofvarious BMS devices and the control parameters associated therewith.Building control services module 160 may be configured to achievecontrol setpoints using building equipment with a relatively lowerenergy cost (e.g., by causing airflow between connected building zones)in order to reduce the loading on building equipment with a relativelyhigher energy cost (e.g., chillers and roof top units). For example,building control services module 160 may be configured to move warmerair from higher elevation zones to lower elevation zones by establishingpressure gradients between connected building zones.

Referring now to FIG. 4 , another block diagram illustrating a portionof BMS 11 in greater detail is shown, according to some embodiments. BMS11 can be implemented in building 10 to automatically monitor andcontrol various building functions. BMS 11 is shown to include BMScontroller 12 and a plurality of building subsystems 428. Buildingsubsystems 428 are shown to include a building electrical subsystem 434,an information communication technology (ICT) subsystem 436, a securitysubsystem 438, a HVAC subsystem 440, a lighting subsystem 442, alift/escalators subsystem 432, and a fire safety subsystem 430. Invarious embodiments, building subsystems 428 can include fewer,additional, or alternative subsystems. For example, building subsystems428 may also or alternatively include a refrigeration subsystem, anadvertising or signage subsystem, a cooking subsystem, a vendingsubsystem, a printer or copy service subsystem, or any other type ofbuilding subsystem that uses controllable equipment and/or sensors tomonitor or control building 10.

Each of building subsystems 428 can include any number of devices,controllers, and connections for completing its individual functions andcontrol activities. HVAC subsystem 440 can include many of the samecomponents as HVAC system 20, as described with reference to FIGS. 2-3 .For example, HVAC subsystem 440 can include a chiller, a boiler, anynumber of air handling units, economizers, field controllers,supervisory controllers, actuators, temperature sensors, and otherdevices for controlling the temperature, humidity, airflow, or othervariable conditions within building 10. Lighting subsystem 442 caninclude any number of light fixtures, ballasts, lighting sensors,dimmers, or other devices configured to controllably adjust the amountof light provided to a building space. Security subsystem 438 caninclude occupancy sensors, video surveillance cameras, digital videorecorders, video processing servers, intrusion detection devices, accesscontrol devices and servers, or other security-related devices.

Still referring to FIG. 4 , BMS controller 12 is shown to include acommunications interface 407 and a BMS interface 132. Interface 407 mayfacilitate communications between BMS controller 12 and externalapplications (e.g., monitoring and reporting applications 422,enterprise control applications 426, remote systems and applications444, applications residing on client devices 448, etc.) for allowinguser control, monitoring, and adjustment to BMS controller 12 and/orsubsystems 428. Interface 407 may also facilitate communications betweenBMS controller 12 and client devices 448. BMS interface 132 mayfacilitate communications between BMS controller 12 and buildingsubsystems 428 (e.g., HVAC, lighting security, lifts, powerdistribution, business, etc.).

Interfaces 407, 132 can be or include wired or wireless communicationsinterfaces (e.g., jacks, antennas, transmitters, receivers,transceivers, wire terminals, etc.) for conducting data communicationswith building subsystems 428 or other external systems or devices. Invarious embodiments, communications via interfaces 407, 132 can bedirect (e.g., local wired or wireless communications) or via acommunications network 446 (e.g., a WAN, the Internet, a cellularnetwork, etc.). For example, interfaces 407, 132 can include an Ethernetcard and port for sending and receiving data via an Ethernet-basedcommunications link or network. In another example, interfaces 407, 132can include a Wi-Fi transceiver for communicating via a wirelesscommunications network. In another example, one or both of interfaces407, 132 can include cellular or mobile phone communicationstransceivers. In one embodiment, communications interface 407 is a powerline communications interface and BMS interface 132 is an Ethernetinterface. In other embodiments, both communications interface 407 andBMS interface 132 are Ethernet interfaces or are the same Ethernetinterface.

Still referring to FIG. 4 , BMS controller 12 is shown to include aprocessing circuit 134 including a processor 136 and memory 138.Processing circuit 134 can be communicably connected to BMS interface132 and/or communications interface 407 such that processing circuit 134and the various components thereof can send and receive data viainterfaces 407, 132. Processor 136 can be implemented as a generalpurpose processor, an application specific integrated circuit (ASIC),one or more field programmable gate arrays (FPGAs), a group ofprocessing components, or other suitable electronic processingcomponents.

Memory 138 (e.g., memory, memory unit, storage device, etc.) can includeone or more devices (e.g., RAM, ROM, Flash memory, hard disk storage,etc.) for storing data and/or computer code for completing orfacilitating the various processes, layers and modules described in thepresent application. Memory 138 can be or include volatile memory ornon-volatile memory. Memory 138 can include database components, objectcode components, script components, or any other type of informationstructure for supporting the various activities and informationstructures described in the present application. According to someembodiments, memory 138 is communicably connected to processor 136 viaprocessing circuit 134 and includes computer code for executing (e.g.,by processing circuit 134 and/or processor 136) one or more processesdescribed herein.

In some embodiments, BMS controller 12 is implemented within a singlecomputer (e.g., one server, one housing, etc.). In various otherembodiments BMS controller 12 can be distributed across multiple serversor computers (e.g., that can exist in distributed locations). Further,while FIG. 4 shows applications 422 and 426 as existing outside of BMScontroller 12, in some embodiments, applications 422 and 426 can behosted within BMS controller 12 (e.g., within memory 138).

Still referring to FIG. 4 , memory 138 is shown to include an enterpriseintegration layer 410, an automated measurement and validation (AM&V)layer 412, a demand response (DR) layer 414, a fault detection anddiagnostics (FDD) layer 416, an integrated control layer 418, and abuilding subsystem integration later 420. Layers 410-420 can beconfigured to receive inputs from building subsystems 428 and other datasources, determine optimal control actions for building subsystems 428based on the inputs, generate control signals based on the optimalcontrol actions, and provide the generated control signals to buildingsubsystems 428. The following paragraphs describe some of the generalfunctions performed by each of layers 410-420 in BMS 11.

Enterprise integration layer 410 can be configured to serve clients orlocal applications with information and services to support a variety ofenterprise-level applications. For example, enterprise controlapplications 426 can be configured to provide subsystem-spanning controlto a graphical user interface (GUI) or to any number of enterprise-levelbusiness applications (e.g., accounting systems, user identificationsystems, etc.). Enterprise control applications 426 may also oralternatively be configured to provide configuration GUIs forconfiguring BMS controller 12. In yet other embodiments, enterprisecontrol applications 426 can work with layers 410-420 to optimizebuilding performance (e.g., efficiency, energy use, comfort, or safety)based on inputs received at interface 407 and/or BMS interface 132.

Building subsystem integration layer 420 can be configured to managecommunications between BMS controller 12 and building subsystems 428.For example, building subsystem integration layer 420 may receive sensordata and input signals from building subsystems 428 and provide outputdata and control signals to building subsystems 428. Building subsystemintegration layer 420 may also be configured to manage communicationsbetween building subsystems 428. Building subsystem integration layer420 translate communications (e.g., sensor data, input signals, outputsignals, etc.) across a plurality of multi-vendor/multi-protocolsystems.

Demand response layer 414 can be configured to optimize resource usage(e.g., electricity use, natural gas use, water use, etc.) and/or themonetary cost of such resource usage in response to satisfy the demandof building 10. The optimization can be based on time-of-use prices,curtailment signals, energy availability, or other data received fromutility providers, distributed energy generation systems 424, fromenergy storage 427, or from other sources. Demand response layer 414 mayreceive inputs from other layers of BMS controller 12 (e.g., buildingsubsystem integration layer 420, integrated control layer 418, etc.).The inputs received from other layers can include environmental orsensor inputs such as temperature, carbon dioxide levels, relativehumidity levels, air quality sensor outputs, occupancy sensor outputs,room schedules, and the like. The inputs may also include inputs such aselectrical use (e.g., expressed in kWh), thermal load measurements,pricing information, projected pricing, smoothed pricing, curtailmentsignals from utilities, and the like.

According to some embodiments, demand response layer 414 includescontrol logic for responding to the data and signals it receives. Theseresponses can include communicating with the control algorithms inintegrated control layer 418, changing control strategies, changingsetpoints, or activating/deactivating building equipment or subsystemsin a controlled manner. Demand response layer 414 may also includecontrol logic configured to determine when to utilize stored energy. Forexample, demand response layer 414 may determine to begin using energyfrom energy storage 427 just prior to the beginning of a peak use hour.

In some embodiments, demand response layer 414 includes a control moduleconfigured to actively initiate control actions (e.g., automaticallychanging setpoints) which minimize energy costs based on one or moreinputs representative of or based on demand (e.g., price, a curtailmentsignal, a demand level, etc.). In some embodiments, demand responselayer 414 uses equipment models to determine an optimal set of controlactions. The equipment models can include, for example, thermodynamicmodels describing the inputs, outputs, and/or functions performed byvarious sets of building equipment. Equipment models may representcollections of building equipment (e.g., subplants, chiller arrays,etc.) or individual devices (e.g., individual chillers, heaters, pumps,etc.).

Demand response layer 414 may further include or draw upon one or moredemand response policy definitions (e.g., databases, XML, files, etc.).The policy definitions can be edited or adjusted by a user (e.g., via agraphical user interface) so that the control actions initiated inresponse to demand inputs can be tailored for the user's application,desired comfort level, particular building equipment, or based on otherconcerns. For example, the demand response policy definitions canspecify which equipment can be turned on or off in response toparticular demand inputs, how long a system or piece of equipment shouldbe turned off, what setpoints can be changed, what the allowable setpoint adjustment range is, how long to hold a high demand setpointbefore returning to a normally scheduled setpoint, how close to approachcapacity limits, which equipment modes to utilize, the energy transferrates (e.g., the maximum rate, an alarm rate, other rate boundaryinformation, etc.) into and out of energy storage devices (e.g., thermalstorage tanks, battery banks, etc.), and when to dispatch on-sitegeneration of energy (e.g., via fuel cells, a motor generator set,etc.).

Integrated control layer 418 can be configured to use the data input oroutput of building subsystem integration layer 420 and/or demandresponse later 414 to make control decisions. Due to the subsystemintegration provided by building subsystem integration layer 420,integrated control layer 418 can integrate control activities of thesubsystems 428 such that the subsystems 428 behave as a singleintegrated supersystem. In some embodiments, integrated control layer418 includes control logic that uses inputs and outputs from a pluralityof building subsystems to provide greater comfort and energy savingsrelative to the comfort and energy savings that separate subsystemscould provide alone. For example, integrated control layer 418 can beconfigured to use an input from a first subsystem to make anenergy-saving control decision for a second subsystem. Results of thesedecisions can be communicated back to building subsystem integrationlayer 420.

Integrated control layer 418 is shown to be logically below demandresponse layer 414. Integrated control layer 418 can be configured toenhance the effectiveness of demand response layer 414 by enablingbuilding subsystems 428 and their respective control loops to becontrolled in coordination with demand response layer 414. Thisconfiguration may advantageously reduce disruptive demand responsebehavior relative to conventional systems. For example, integratedcontrol layer 418 can be configured to assure that a demandresponse-driven upward adjustment to the setpoint for chilled watertemperature (or another component that directly or indirectly affectstemperature) does not result in an increase in fan energy (or otherenergy used to cool a space) that would result in greater total buildingenergy use than was saved at the chiller.

Integrated control layer 418 can be configured to provide feedback todemand response layer 414 so that demand response layer 414 checks thatconstraints (e.g., temperature, lighting levels, etc.) are properlymaintained even while demanded load shedding is in progress. Theconstraints may also include setpoint or sensed boundaries relating tosafety, equipment operating limits and performance, comfort, fire codes,electrical codes, energy codes, and the like. Integrated control layer418 is also logically below fault detection and diagnostics layer 416and automated measurement and validation layer 412. Integrated controllayer 418 can be configured to provide calculated inputs (e.g.,aggregations) to these higher levels based on outputs from more than onebuilding subsystem.

Automated measurement and validation (AM&V) layer 412 can be configuredto verify that control strategies commanded by integrated control layer418 or demand response layer 414 are working properly (e.g., using dataaggregated by AM&V layer 412, integrated control layer 418, buildingsubsystem integration layer 420, FDD layer 416, or otherwise). Thecalculations made by AM&V layer 412 can be based on building systemenergy models and/or equipment models for individual BMS devices orsubsystems. For example, AM&V layer 412 may compare a model-predictedoutput with an actual output from building subsystems 428 to determinean accuracy of the model.

Fault detection and diagnostics (FDD) layer 416 can be configured toprovide on-going fault detection for building subsystems 428, buildingsubsystem devices (i.e., building equipment), and control algorithmsused by demand response layer 414 and integrated control layer 418. FDDlayer 416 may receive data inputs from integrated control layer 418,directly from one or more building subsystems or devices, or fromanother data source. FDD layer 416 may automatically diagnose andrespond to detected faults. The responses to detected or diagnosedfaults can include providing an alert message to a user, a maintenancescheduling system, or a control algorithm configured to attempt torepair the fault or to work-around the fault.

FDD layer 416 can be configured to output a specific identification ofthe faulty component or cause of the fault (e.g., loose damper linkage)using detailed subsystem inputs available at building subsystemintegration layer 420. In other exemplary embodiments, FDD layer 416 isconfigured to provide “fault” events to integrated control layer 418which executes control strategies and policies in response to thereceived fault events. According to some embodiments, FDD layer 416 (ora policy executed by an integrated control engine or business rulesengine) may shut-down systems or direct control activities around faultydevices or systems to reduce energy waste, extend equipment life, orassure proper control response.

FDD layer 416 can be configured to store or access a variety ofdifferent system data stores (or data points for live data). FDD layer416 may use some content of the data stores to identify faults at theequipment level (e.g., specific chiller, specific AHU, specific terminalunit, etc.) and other content to identify faults at component orsubsystem levels. For example, building subsystems 428 may generatetemporal (i.e., time-series) data indicating the performance of BMS 11and the various components thereof. The data generated by buildingsubsystems 428 can include measured or calculated values that exhibitstatistical characteristics and provide information about how thecorresponding system or process (e.g., a temperature control process, aflow control process, etc.) is performing in terms of error from itssetpoint. These processes can be examined by FDD layer 416 to exposewhen the system begins to degrade in performance and alert a user torepair the fault before it becomes more severe.

Regional Intelligence for Building Management Systems Referring now toFIG. 5 , a diagram of a system 500 including a computing system 502communicable with multiple building management systems (shown as BMSs 11a-11 m) is shown, according to some embodiments. FIG. 5 illustrates thatthe multiple BMSs 11 a-11 m can be located in a variety of geographicregions, for example various regions of a country (e.g., the UnitedStates as shown), various regions of a continent, various regions of theworld, etc. Any number of BMSs can be included in any embodiment.

The BMSs 11 a-11 m can be various different building management systemssimilar to BMS 11 described above, adapted to serve buildings indifferent geographic regions as illustrated in FIG. 5 . For example, BMS11 i may be located in a relatively warm climate as compared to BMS 11d, and may therefore include more cooling-related components as comparedto BMS 11 d (which may include more heating-related components). Asanother example, BMS 11 b may be located in a region where the primaryenergy sources is electricity from an energy grid, while BMS 11 f may belocated in a region where natural gas is a widely-available source ofenergy for consumption by building equipment, with the result thatsuitable equipment used in the different BMSs is different in differentregions. Other differences can be rooted in legacy equipment, buildingage, culture, etc. which drives differences common to different regions(e.g., radiant heat may be often used in one region whereas forced airheat may be used in another region). BMSs 11 a-m may therefore bedifferent from one another in ways driven by association of the BMSs 11a-m with different geographic regions. Different equipment, conditions,climate, architecture, etc. can result in a need for differentconfigurations, parameters, settings, installations, devices, etc. forthe different BMSs 11 a-m in different regions.

The computing system 502 is communicable with the BMSs 11 a-m (e.g., viathe Internet) and can be positioned in any geographic region or may bedistributed across multiple geographic regions. The computing system 502and provide various remote services, support, functions, etc. to thevarious BMSs 11 a-m. The computing system 502 can also generate highlevel insights using data from the multiple BMSs 11 a-m, for examplefacilitating enabling comparisons of performance across BMSs 11 a-m. Insome embodiments, the computing system 502 is configured to providesetpoints, control decisions, configuration parameters, control logic,automated troubleshooting routines, etc. to the BMSs 11 a-m to affectoperation of equipment and devices of such BMSs 11 a-m. The computingsystem 502 may also be configured to provide (e.g., host) one or moregraphical user interfaces accessible via a user device (e.g., smartphonevia an Internet browser or mobile application) and displayinginformation to such users associated with configuring, troubleshooting,and/or otherwise monitoring or managing one or more of the BMSs 11 a-m.

The system 500, building automation information can be communicated withthe multiple BMSs 11 a-m and the computing system 502, for example viaone or more networks (e.g., via the Internet). Information can flowdirectly between the computing system 502, and the BMSs 11 a-m, betweendifferent BMSs 11 a-m, from a first BMS (e.g., BMS 11 a) to a second BMS(e.g., BMS 11 b) via the computing system 502, and/or otherwisecommunicated amongst the BMSs 11 a-m and the computing system 502.Building automation can include sensor data (e.g., measurements oftemperature, humidity, air quality, pressure, air flow, vibration,luminosity), meter data (e.g., power usage, energy consumption, gasusage, water usage, etc.), setpoints (e.g., temperature setpoints,airflow setpoints, etc.), pollution or emissions data (e.g., datarelating to carbon emissions attributable to a building managementsystem), configuration parameters and device capacities (e.g., networkidentities, CPU usage, memory usage, bandwidth requirements, etc.),equipment models, digital twin information, building utlilization (e.g.,occupancy, events, productivity), operational changes, recommendations,troubleshooting instructions, insights, alerts, faults, alarms, etc.Various information relating to one or more BMSs 11 a-m and/or computingsystem 502 can be communicated in the system 500 in various embodiments.

In some embodiments, the system 500 is configured to automaticallyadjust the building automation information in accordance with differentregional terminology used by the multiple BMSs 11 a-m (and, in someembodiments, by the computing system 502). That is, building automationinformation can be automatically translated between different sets oflocal terminology when communicated within the system 500 which includescomponents distributed across multiple geographic regions as shown inFIG. 5 .

In some embodiments, the computing system 502 includes circuitryprogrammed (e.g., with instructions stored in non-transitorycomputer-readable media) configured provide such adjustments(translations, etc.) of regional terminology. In other embodiments, theBMSs 11 a-m may include, locally at different geographic regions,circuitry programmed to translate inbound communications into a suitableregional terminology for that region and/or to translate outboundcommunications into suitable regional terminology (orgenericized/normalized terminology) for an intended recipient (e.g.,computing system 502) of such outbound communications.

Automated translations may be performed using deep learning models,large language models, natural language processing, neural networks,machine learning models, artificial intelligence, etc. in variousembodiments. In some embodiments, one or more artificial intelligencemodels (e.g., machine learning models) are used and are trained on datacollected via the BMSs 11 a-m. For example, the BMSs 11 a-m may promptlocal users in the multiple geographic regions to answer questionsrelating to different regional terminology used by the users. Forexample, users in different regions may prompted input the local namefor a particular type of equipment, equipment parameter, physicalphenomenon, etc. and may input different terms due to regionaldifferences in terminology. The prompt for information may includeillustrations, images, graphics, etc., for example without words thereinin order to avoid guiding the user to a different terminology thantypically used by that user. Data can thereby be collected from users atmultiple geographic regions that is indicative of terminology used inthe multiple geographic regions. A training process (e.g., supervisedlearning, unsupervised learning) can then be applied using such data totrain one or more artificial intelligence models to automaticallytranslate between terminology used in such regions. Such a training andfeedback process can be used to train (e.g., fine-tune) at least onegenerative AI model, for example according to the teachings of, theentire disclosure of which is incorporated by reference herein.

In some embodiments, automated translations are provided using tables orother maps storing relationships between terms as used in differentregions. For example, for a given physical parameter, a genericidentifier can be stored along with the different words used to describesuch a parameter in the different geographic regions. Using suchterminology dictionaries (e.g., populated using AI using a similarapproach as above, populated by human experts, etc.) circuitry can beprogrammed to automatically uses such dictionaries to provide automatedtranslations between geographic regions.

In some embodiments, these features enable building equipment of atleast a first building automation system (e.g., any one of BMSs 11 a-m)to be controlled based on inputs provided by a first user in a firstgeographic region using a first regional terminology associated with thefirst geographic region and inputs provided by a second user in a secondgeographic region using a second regional terminology associated withthe second geographic region. In some embodiments, the correspondingbuilding equipment is at the first geographic region and a manufacturersupport person or other service provider is at the second geographicregion, enabling support across geographic regions while persons at bothregions are able to work natively within the system 500 in terminologyused in their respective regions. The system 500 seamlessly handlesinputs in different regional terminologies and adjust operation ofequipment accordingly.

In some embodiments, each user of the system 500 has a personalizedterminology with individualized translations using models as describedabove. The terminology can be user-defined, trained via a set-up stagefor a user account, etc. Such a feature enables user-level terminologiesas an alternative or in addition to regional terminologies.

In some embodiments, the system 500 (e.g., the computing system 502, theBMSs 11 a-m, and/or some combination thereof) is configured to normalizepoint labels for data provided by the plurality of building automationsystems to facilitate analysis of aggregations of the data from theplurality of building automation systems. Data created in differentterminologies can be automatically translated into a single terminologyusing the teachings herein, such that single terminology can then beused in analyses of aggregated data (e.g., for enterprise-wide analysis,for comparison of regions, for comparison of BMSs, etc.). Performanceindicators, recommendations, control settings, and other insights drivenby analysis of aggregated can be transmitted back to different BMSs 11a-m along with translation into regional terminologies suitable for therecipients of such information.

The system 500 thereby enables seamless interoperability of buildingautomation systems across multiple languages, dialects, regionalterminologies, etc., thereby enabling broad geographic distribution(e.g., worldwide, nationwide, etc.) of equipment (e.g., building networkdevices, controllers, gateways, building equipment) and services (e.g.,software-as-a-service, energy-management-as-a-service,carbon-neutral-as-a-service, net-zero-as-a-service) while eliminatingconfusion or barriers driven by terminological differences acrossgeographic regions.

Referring now to FIG. 6 , a flowchart of a process 600 is shown,according to some embodiments. The process 600 can be executed by any ofthe various elements of the system 500, in isolation and/or incombination, in various embodiments. In some embodiments, the process600 is executed by or using a personal computing device, for example apersonal computing device in communication with a remote computingsystem (e.g., cloud system). The personal computing device may be asmartphone, tablet, laptop computer, desktop computer, virtual oraugmented reality headset, etc. in various embodiments. Some or all ofprocess 600 can execute at the edge (e.g., on a unit of buildingequipment, on a gateway or controller of a building management system,etc.).

At step 600, an artificial intelligence tool is selected from a set ofavailable artificial intelligence tools based on a geographic region,for example a geographic region of a building automation system or unitof building equipment. The geographic region can be determinedautomatically, for example using global positioning satellite (GPS)coordinates from a GPS chip included with the bundling automation systemor unit of building equipment, using an IP address or other networkinginformation relating to the building automation system or unit ofbuilding equipment, etc. In some embodiments, a graphical user interfaceis provided with allows a user to select a geographic region via thegraphical user interface (e.g., from a list, from a map view, etc.).

The set of available artificial intelligence tools can include one ormore artificial intelligence tool associated with each of the selectablegeographic regions. The artificial intelligence tools can be configuredto provide various building configuration, control, and managementfeatures, for example described in other sections of the presentapplication. In the embodiments shown in FIG. 6 , at least a subset ofthe artificial intelligence tools relate to troubleshootingapplications, for example to generating, ranking, filtering, etc.troubleshooting steps to facilitate system reconfiguration, testing,validation, etc. to improve system performance of a building automationsystem. The artificial intelligence tools can include models trained viasupervised learning, decision tree learning, association rule learning,inductive logic programming, support vector machines, unsupervisedlearning, clustering, similarity and metric learning, sparse dictionarylearning, genetic algorithms, reinforcement learning, Bayesian networks,neural networks, deep learning manifold learning, etc.

In some embodiments, each artificial intelligence tool is or includes adifferent generative artificial intelligence model, for example agenerative artificial models fine-tuned based on different, regionaltraining data to be adapted to regional details (e.g., regional servicedata, regional building operating data, regional warranty data, etc.).For example, model fine-tuning based on service and/or warranty data asin U.S. Provisional Patent Application No. 63/470,754 filed Jun. 2,2023, incorporated by reference in its entirety herein, can beimplemented to separately fine tune a generative AI model to haveregional intelligence by separating the service and/or warranty data bygeographical region, using the service and/or warranty data for a firstregion to fine-tune the generative AI model according to the teachingstherein to get a first fine-tuned model, using the service and/orwarranty data for a second region to fine-tune the generative AI modelaccording to the teachings therein to get a second fine-tuned model forthe second region, etc. for the number of desired geographic regions.

In some embodiments, each artificial intelligence tool includes asgenerative artificial intelligence model and a model augmentationfunctions (e.g., feature generation algorithm, translation tool, pre-and/or post-processing module, etc.). The different model augmentationfunctions augment the full generative artificial intelligence models,and may be substantially smaller than and/or more efficient (e.g.,easier, faster, on less training data, less computationally expensive,etc.) to train, fit, adapted, etc. as compared to the generativeartificial intelligence model, while operating to adjust operation ofthe generative artificial intelligence model to adapt to particulargeographic/regional characteristics (e.g., by adjust vectors input toand/or output from the at least one generative AI model). Accordingly,in some embodiments, the same generative artificial intelligence modelmay be used with different model augmentation functions, where eachmodel augmentation function is associated with a different geographicregion such that multiple artificial intelligence tools can be providedfor multiple geographic regions without requiring memory-intensivestorage of multiple different generative AI models.

Selecting one or more artificial intelligence tools associated with aselected geographic region ensures that the artificial intelligencetools are suitable for the corresponding region. For example, theartificial intelligence tools for a region may be trained using trainingdata specific to that region. As another example, the artificialintelligence tools may be designed or provided based on types ofequipment typical in the corresponding region. As another example, theartificial intelligence tools may be adapted to use terminology or othercontext associated with a corresponding geographic region (e.g., bytraining, fine-tuning, or augmenting each artificial intelligence toolusing training data from the corresponding geographic region). Step 602thereby enables the geographic location of the building automationsystem, building equipment, or other relevant device to drive selectionof an appropriate artificial intelligence tool (e.g., AI model, machinelearning model, neural network, etc.) that may be better suited to thatgeographic region than an artificial intelligence tool intended to workacross many geographic regions (e.g., thus providing more accurateclassifications, more reliable predictions or troubleshootingrecommendations, lower false-positive and false-negative rates, moreoptimal results in terms of energy, cost, and/or emissions savings,better occupant comfort, etc.). In some embodiments, the set ofartificial intelligence tools is further narrowed based on a buildingdomain to be trouble shot (e.g., HVAC, fire, security, access control,lighting, etc.), such that the selection in step 602 is further oralternatively based on building domain.

In the embodiments of FIG. 6 , at step 604 troubleshooting options areranked for the building automation system by applying an artificialintelligence tool selected in step 602 to data associated with thebuilding automation system. The data can include sensor measurements,meter data, setpoints, settings, configuration parameters, digital twininformation, equipment models, weather information, building utilizationinformation, and/or other information relating to the buildingautomation system in various embodiments. The artificial intelligencetool can be trained (e.g., using results of historical troubleshooting,using data representing successful configurations of other buildingmanagement systems, in a supervised training approach, based on suchinformation and/or supervised training particularly associated with therelevant geographic region) to rank troubleshooting options from a listof available troubleshooting options, for example using a neural networkor other AI model that outputs a likelihood that each troubleshootingoption will lead to resolution of an issue or otherwise provide aperformance improvement. A ranked set of troubleshooting options canthus be output from step 604.

At step 606, at least a first troubleshooting option is implemented. Thefirst troubleshooting option may be the highest-ranked by the artificialintelligence tool, e.g., ranked higher than other options in step 604.Implementing the troubleshooting option may include providinginstructions or recommendations to a technician, for exampleautomatically generating a work order for a technician to carry outactions to implement the troubleshooting options. Implementing thetroubleshooting option may include automatically changing control logic,settings, setpoints, configurations, parameters, etc. of buildingequipment or devices (e.g., in software), thereby automaticallyaffecting operating of building equipment or devices to implementing atroubleshooting option (e.g., in a closed-loop manner). Implementing thetroubleshooting option can include installing one or more devices orunits or building equipment, for example to correct deficiencies in anexisting building management system. Implementing the troubleshootingoption can include adjusting (e.g., moving, reorienting, rearranging,reconnecting, rewiring, etc.) an installation of one or more devices orunits of building equipment. All of these and other possibilities arewithin the scope of the present disclosure.

By following process 600, troubleshooting options suitable for aparticular location and prioritized using artificial intelligence can beimplemented. Process 600 thereby solves challenges whereby typicaltraining materials or troubleshooting suggestions are published from acentral source (e.g., a manufacturer) in a single list which does notaccount for regional variation or have regional knowledge that canrender troubleshooting options suitable for one region unsuitable foranother region. Efficient troubleshooting and other services can thus beenabled by the features of process 600, thereby efficiently solvingtechnical issues with operation of building management systems andbuilding equipment.

While the example of process 600 refers to troubleshooting options,process 600 can also be used to provide other workflow recommendationsand execution. For example, process 600 can be adapted to automaticallyprovide, with regional intelligence according to the teachings process600, building system design and engineering recommendations,installation workflow instructions, building system commissioning,testing, and validation instructions or control, maintenancerecommendations, etc. to provide building engineers, managers,technicians, etc. with regionally-tuned information through the buildinglife-cycle. For example, the various service recommendations, virtualassistant applications, etc. described in U.S. Provisional ApplicationNo. 63/466,603, filed May 15, 2023, incorporated by reference in itsentirety herein, can be provided by selecting between differentgeographic-region-specific artificial intelligence tools as in process600 so as to provide regionally-intelligent virtual assistance with avariety of installation, commissioning, and service tasks for buildingsystems.

Configuration of Exemplary Embodiments

The construction and arrangement of the systems and methods as shown inthe various exemplary embodiments are illustrative only. Although only afew embodiments have been described in detail in this disclosure, manymodifications are possible (e.g., variations in sizes, dimensions,structures, shapes and proportions of the various elements, values ofparameters, mounting arrangements, use of materials, colors,orientations, etc.). For example, the position of elements can bereversed or otherwise varied and the nature or number of discreteelements or positions can be altered or varied. Accordingly, all suchmodifications are intended to be included within the scope of thepresent disclosure. The order or sequence of any process or method stepscan be varied or re-sequenced according to alternative embodiments.Other substitutions, modifications, changes, and omissions can be madein the design, operating conditions and arrangement of the exemplaryembodiments without departing from the scope of the present disclosure.

The present disclosure contemplates methods, systems and programproducts on any machine-readable media for accomplishing variousoperations. The embodiments of the present disclosure can be implementedusing existing computer processors, or by a special purpose computerprocessor for an appropriate system, incorporated for this or anotherpurpose, or by a hardwired system. Embodiments within the scope of thepresent disclosure include program products comprising machine-readablemedia for carrying or having machine-executable instructions or datastructures stored thereon. Such machine-readable media can be anyavailable media that can be accessed by a general purpose or specialpurpose computer or other machine with a processor. By way of example,such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROMor other optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to carry or storedesired program code in the form of machine-executable instructions ordata structures and which can be accessed by a general purpose orspecial purpose computer or other machine with a processor. Combinationsof the above are also included within the scope of machine-readablemedia. Machine-executable instructions include, for example,instructions and data which cause a general purpose computer, specialpurpose computer, or special purpose processing machines to perform acertain function or group of functions.

Although the figures show a specific order of method steps, the order ofthe steps may differ from what is depicted. Also two or more steps canbe performed concurrently or with partial concurrence. Such variationwill depend on the software and hardware systems chosen and on designerchoice. All such variations are within the scope of the disclosure.Likewise, software implementations could be accomplished with standardprogramming techniques with rule based logic and other logic toaccomplish the various connection steps, processing steps, comparisonsteps and decision steps.

What is claimed is:
 1. A method for troubleshooting a buildingautomation system, comprising: selecting an artificial intelligence toolfrom a set of available artificial intelligence tools based on thegeographic region of the building automation system; rankingtroubleshooting options for the building automation system by applyingthe artificial intelligence tool to data associated with the buildingautomation system; and implementing at least a first troubleshootingoption, the first troubleshooting option ranked higher than a remainderof the troubleshooting options by the artificial intelligence tool. 2.The method of claim 1, further comprising ignoring one or more of thetroubleshooting options based on the geographic region of the buildingautomation system.
 3. The method of claim 1, wherein implementing at theleast the first troubleshooting option comprises automatically adjustingoperation of building equipment operated by the building automationsystem.
 4. The method of claim 1, wherein implementing at the least thefirst troubleshooting option comprises adjusting an installation of oneor more devices of the building automation system.
 5. The method ofclaim 1, wherein implementing at the least the first troubleshootingoption comprises changing configuration parameters of one or moredevices of the building automation system.
 6. The method of claim 1,further comprises displaying the first troubleshooting option to a uservia a mobile application.
 7. The method of claim 1, wherein selectingthe artificial intelligence tool is further based on a domain ofbuilding equipment to be troubleshot.
 8. The method of claim 1,comprising training the available artificial intelligence tools areassociated with different geographic regions and trained on differentdata sets associated with the different geographic regions.
 9. Themethod of claim 1, wherein the available artificial intelligence toolscomprise: a first artificial intelligence tool comprising a firstgenerative artificial intelligence model fine-tuned or augmented usingfirst training data from a first geographic region; a second artificialintelligence tool comprising a second generative artificial intelligencemodel fine-tuned or augmented using second training data from a secondgeographic region.
 10. One or more non-transitory computer-readablemedia storing instructions that, when executed by one or moreprocessors, cause the one or more processors to perform operationscomprising: selecting, based on a location, an artificial intelligencetool from a set of available artificial intelligence tools associatedwith a plurality of geographic regions, the available artificialintelligence tools trained using different training data associated withthe different geographic regions; ranking troubleshooting options byapplying the artificial intelligence tool to data associated with thelocation; and causing implementation of at least a first troubleshootingoption, the first troubleshooting option ranked higher than a remainderof the troubleshooting options by the artificial intelligence tool. 11.The one or more non-transitory computer-readable media of claim 10,wherein the location corresponds to a building, and wherein thetroubleshooting options comprise maintenance or service tasks forbuilding equipment serving the building.
 12. The one or morenon-transitory computer-readable media of claim 10, wherein theavailable artificial intelligence tools comprise: a first artificialintelligence tool comprising a generative artificial intelligence modeland a first augmentation function; and a second artificial intelligencetool comprising the generative artificial intelligence model and secondaugmentation function.
 13. The one or more non-transitorycomputer-readable media of claim 10, wherein selecting the artificialintelligence tool is further based on a domain of building equipment tobe troubleshot
 14. The one or more non-transitory computer-readablemedia of claim 10, wherein causing implementation of at the least thefirst troubleshooting option comprises automatically adjusting operationof building equipment at the location.
 15. A method, comprising:communicating building automation information with a plurality ofbuilding automation systems located in a plurality of geographicregions; automatically adjusting the building automation information inaccordance with different regional terminology used by the plurality ofbuilding automation systems; and controlling building equipment with atleast a first building automation system of the plurality of buildingautomation systems based on: inputs provided by a first user in a firstgeographic region using a first regional terminology associated with thefirst geographic region; and inputs provides by a second user in asecond geographic region using a second regional terminology associatedwith the second geographic region.
 16. The method of claim 8, whereinthe first building automation system and the first user are located atthe first geographic region and the second user is located at the secondgeographic region.
 17. The method of claim 8, wherein the first user isassociated with the first building automation system and the second userprovides support for the plurality of building automation systems. 18.The method of claim 8, wherein automatically adjusting the buildingautomation information in accordance with the different regionalterminology comprises applying a machine learning model to the buildingautomation information.
 19. The method of claim 11, further comprising:prompting, via the plurality of building automation systems, users atthe plurality of geographic regions to answer questions relating to thedifferent regional terminology used by the users; and training themachine learning model based on answers to the questions.
 20. The methodof claim 8, wherein automatically adjusting the building automationinformation in accordance with the different regional terminology usedby the plurality of building automation systems comprises normalizingpoint labels for data provided by the plurality of building automationsystems to facilitate analysis of aggregations of the data from theplurality of building automation systems.